Impact of Tisagenlecleucel Chimeric Antigen Receptor (CAR)-T Cell Therapy Product Attributes on Clinical Outcomes in Adults with Relapsed or Refractory Diffuse Large B-Cell Lymphoma (r/r DLBCL)
Bibliographic record
Abstract
Background: In the phase 2 JULIET trial, tisagenlecleucel, an anti-CD19 CAR-T cell therapy, demonstrated durable responses and manageable safety in adult patients (pts) with r/r DLBCL. Here, we examine the impact of key product cellular attributes of tisagenlecleucel on clinical outcomes. Methods: JULIET is a single-arm, global, phase 2 trial of tisagenlecleucel in adult pts with r/r DLBCL. Samples from 115 tisagenlecleucel individual products were examined at the end of manufacturing at the batch release testing for various product attributes (Table). Additional detailed immunophenotyping for 66 attributes was conducted on previously frozen product samples via flow cytometry (FC). For each cell population of CAR+ T cells, the percentage and absolute number of the subpopulation were analyzed. Univariate and multivariate analyses were performed to evaluate effects of product attributes and CAR+ T-cell phenotypes on efficacy (Month 3 response [M3R], duration of response [DOR], progression-free survival [PFS], overall survival [OS]) and safety (cytokine release syndrome [CRS] and neurological events [NE], grade 0-2 [low] vs 3-4 [severe]). Several exploratory approaches, including machine learning methods (eg, elastic net and random forest), were pursued in conjunction with logistic regression to identify a set of variables associated with clinical outcomes. We included clinically relevant characteristics evaluated at baseline per protocol (LDH, CRP, and tumor volume) with the product attributes in multivariate modeling. Logistic regression was used to model M3R, CRS, and NE. Cox regression was used to model DOR, PFS, and OS. Results: As of December 11, 2018, 115 pts were infused and evaluable. The median T-cell transduction efficiency by FC was 28% (range, 5.3-63.2%); no relationship of these attributes with efficacy (M3R, DOR, PFS, or OS) or safety (severe CRS or NE) was observed. The percentage of viable cells had no impact on efficacy or safety outcomes; this was anticipated since tisagenlecleucel dose is formulated based on the number of viable CAR+ T cells. Tisagenlecleucel demonstrated in vitro functional activity upon CD19-specific stimulation, as evidenced by IFNγ release, with a wide range among different batches (range, 23.7-938 fg/CAR+ cell). Durable responses were observed across the entire range of IFNγ release; high IFNγ release was not associated with severe CRS or NE. The median ratio of CAR+ CD4+ to CD8+ cells was 3.70 (range, 0.26-65.3); no relationship with clinical outcomes was observed (Figure). CAR+ T cells showed variability in T-cell phenotypes, with central memory (CM) cells as the predominant subpopulation of both CD4+ and CD8+ CAR+ T cells (Figure). The majority of CAR+ T cells were highly activated (co-expressing HLA-DR and CD38), as measured by FC. Relative and absolute number of less mature T cells (naive and CM T cells) in the product did not correlate with efficacy. There was no significant correlation between cell populations and efficacy on multivariate analyses. For CRS, the total number of certain CD4+ T cells expressing activation markers (HLA-DR+, CD25+, or HLA-DR+CD38+) and CM cells showed trends of correlation with more severe CRS, but none of these were significant after p-value adjustment; in a multivariate regression model adjusted for LDH and other clinically relevant factors, HLA-DR+ CD38+CD4+ T cells showed a correlation with severe CRS. Correlation analyses did not reveal product attributes significantly related to severe NE. Conclusions: In JULIET, tisagenlecleucel CAR-T cell product attributes had no significant impact on efficacy or NE; the total number of activated CD4+ cells infused positively correlated with higher-grade CRS. There is great variability in the product attributes, especially with respect to T-cell phenotypes, though this variability appears to play a minor role on efficacy. Additional analyses with larger data sets are required to confirm these findings. ClinicalTrials.gov Identifier: NCT02445248. Disclosures Bachanova: Novartis: Research Funding; Gamida Cell: Research Funding; GT Biopharma: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Incyte: Research Funding. Tam:BeiGene: Honoraria; Janssen: Honoraria, Research Funding; Roche: Honoraria; Novartis: Honoraria; AbbVie: Honoraria, Research Funding. Jaeger:Novartis, Roche, Sandoz: Consultancy; AbbVie, Celgene, Gilead, Novartis, Roche, Takeda Millennium: Research Funding; Amgen, AbbVie, Celgene, Eisai, Gilead, Janssen, Novartis, Roche, Takeda Millennium, MSD, BMS, Sanofi: Honoraria; Celgene, Roche, Janssen, Gilead, Novartis, MSD, AbbVie, Sanofi: Membership on an entity's Board of Directors or advisory committees. McGuirk:Novartis: Research Funding; Fresenius Biotech: Research Funding; Astellas: Research Funding; Bellicum Pharmaceuticals: Research Funding; Kite Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Gamida Cell: Research Funding; Pluristem Ltd: Research Funding; ArticulateScience LLC: Other: Assistance with manuscript preparation; Juno Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Holte:Novartis: Honoraria, Other: Advisory board. Waller:Amgen: Consultancy; Kalytera: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Other: Travel expenses, Research Funding; Cerus Corporation: Other: Stock, Patents & Royalties; Chimerix: Other: Stock; Cambium Oncology: Patents & Royalties: Patents, royalties or other intellectual property . Jaglowski:Juno: Consultancy, Other: advisory board; Kite: Consultancy, Other: advisory board, Research Funding; Novartis: Consultancy, Other: advisory board, Research Funding; Unum Therapeutics Inc.: Research Funding. Bishop:CRISPR Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Andreadis:Celgene: Research Funding; Novartis: Research Funding; Jazz Pharmaceuticals: Consultancy; Roche: Equity Ownership; Pharmacyclics: Research Funding; Merck: Research Funding; Gilead: Consultancy; Kite: Consultancy; Genentech: Consultancy, Employment; Juno: Research Funding. Foley:Celgene: Speakers Bureau; Amgen: Speakers Bureau; Janssen: Speakers Bureau. Westin:Unum: Research Funding; Genentech: Other: Advisory Board, Research Funding; Novartis: Other: Advisory Board, Research Funding; Janssen: Other: Advisory Board, Research Funding; Juno: Other: Advisory Board; Kite: Other: Advisory Board, Research Funding; 47 Inc: Research Funding; Curis: Other: Advisory Board, Research Funding; MorphoSys: Other: Advisory Board; Celgene: Other: Advisory Board, Research Funding. Fleury:Gilead: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; AstraZeneca: Consultancy. Ho:Janssen: Other: Trial Investigator meeting travel costs; Celgene: Other: Trial Investigator meeting travel costs; La Jolla: Other: Trial Investigator meeting travel costs; Novartis: Other: Trial Investigator meeting travel costs. Mielke:Miltenyi: Consultancy, Honoraria, Other: Travel and speakers fee (via institution), Speakers Bureau; DGHO: Other: Travel support; Jazz Pharma: Honoraria, Other: Travel support, Speakers Bureau; EBMT/EHA: Other: Travel support; Celgene: Honoraria, Other: Travel support (via institution), Speakers Bureau; ISCT: Other: Travel support; Bellicum: Consultancy, Honoraria, Other: Travel (via institution); GILEAD: Consultancy, Honoraria, Other: travel (via institution), Speakers Bureau; Kiadis Pharma: Consultancy, Honoraria, Other: Travel support (via institution), Speakers Bureau; IACH: Other: Travel support. Teshima:Novartis: Honoraria, Research Funding. Salles:Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria. Schuster:Celgene: Consultancy, Honoraria, Research Funding; Acerta: Consultancy, Honoraria, Research Funding; Loxo Oncology: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria, Research Funding; Merck: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Nordic Nano
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".